Here we use a recently developed on-the-fly training scheme [1]}
for building a machine-learned interatomic potential that describes all three
polymorphs of zirconia with near first-principles accuracy. In contrast, no
empirical interatomic potential is capable of seamlessly describing all phases,
nor to reach such accuracy [2]}. We validate the MLFF against the results from first-principles density-functional theory
(DFT) calculations and use it to determine the phase transitions and heat
transport in ZrO\(_2\)  at ambient pressure, accounting for anharmonic
effects to all orders. The agreement with experiments
demonstrates that machine-learned interatomic potentials constructed on the fly
offer an excellent, highly automated solution for accurate and realistic
predictions of anharmonic properties in complex materials.
